Perfusion Assessment Based on Animated Perfusion Imaging

ABSTRACT

An embodiment of a medical imaging system is proposed. The system includes means for providing a sequence of recorded input images each one offering a digital representation at a corresponding instant of a body part being perfused with a contrast agent, each input image including a plurality of visualizing values each one representing a corresponding portion of the body part, and means for associating each sequence of corresponding sets in the input images of at least one visualizing value with a model function of time; the system further includes means for generating a sequence of computed images at further instants, each computed image including a plurality of further visualizing values each one being determined by an instantaneous function-value which is calculated from the associated model function at the corresponding further instant, and means for displaying the sequence of computed images.

PRIORITY CLAIM

This application claims priority from PCT/EP2006/061578, published inEnglish, filed Apr. 13, 2006, which claims priority from European patentApplication No. 05102960.1, filed Apr. 14, 2005, which are incorporatedherein by reference.

TECHNICAL FIELD

An embodiment of the present invention relates to the medical imagingfield. More specifically, an embodiment of the present invention relatesto the display of animated perfusion images.

BACKGROUND

Medical imaging is a well-established technique in the field ofequipment for medical applications. Particularly, this technique iscommonly exploited for the assessment of blood perfusion, which findsuse in several diagnostic applications and especially in ultrasoundanalysis. For this purpose, an ultrasound contrast agent (UCA), forexample, consisting of a suspension of phospholipid-stabilizedgas-filled microbubbles, is administered to a patient. The contrastagent acts as an efficient ultrasound reflector, so that it can beeasily detected by applying ultrasound waves and recording a resultingecho signal. As the contrast agent flows at the same velocity as theblood in the patient, its tracking provides information about theperfusion of the blood in a body part to be analyzed.

Typically, the flow of the contrast agent is monitored by acquiring asequence of consecutive images representing the body part during theperfusion process. More in detail, the value of each pixel of the imagesindicates an intensity of the recorded echo signal for a correspondingportion of the body part. In this way, the sequence of images depictsthe evolution over time of the echo signal throughout the body part.Therefore, the analysis of this sequence of images (for example,displayed on a monitor) provides a quantitative indication of the bloodperfusion in the body part.

However, the quality of the images that are displayed is often quitepoor. Indeed, the value of each pixel of the images exhibits largevariations in time. Moreover, the inevitable presence of speckle grainsin the images produces distracting patterns. The quality of the imagesis also adversely affected by any motion of the body part duringacquisition. Another factor that hinders an effective analysis of theimages is the background echo signals that are superimposed to theuseful information. All of the above makes it very difficult toestablish a correct assessment of the perfusion process; in any case,the results obtained are strongly dependent on the skill of an operatorwho acquires and/or analyzes the sequence of images.

Some attempts have been made to improve the quality of the images inspecific applications. For example, U.S. Pat. No. 6,676,606, which isincorporated by reference, proposes a solution for facilitating theidentification of tiny blood vessels in the body part. For this purpose,the images are processed to reduce the contribution of pixels that arethe same from image to image (for example, associated with stationarytissues) and to make persistent the pixels that instead change fromimage to image (for example, due to a moving microbubble). This enhancesthe visualization of micro-vascular structures.

On the other hand, a quantitative assessment of the perfusion process isprovided by parametric analysis techniques. In this case, the intensityof the echo signal that is recorded over time (for each single pixel orgroup of adjacent pixels) is fitted by a mathematical model function.The model function can then be used to calculate different perfusionparameters, which are indicative of corresponding morphologicalcharacteristics of the respective portion of the body part. Thistechnique has been proposed for the first time in Wei, K., Jayaweera, A.R., Firoozan, S., Linka, A., Skyba, D. M., and Kaul, S., “Quantificationof Myocardial Blood Flow With Ultrasound-Induced Destruction ofMicrobubbles Administered as a Constant Venous Infusion,” Circulation,vol. 97, 1998, which is incorporated by reference. For example, in aso-called destruction-replenishment technique (wherein the contrastagent is destroyed by a flash of sufficient energy, so as to observe itsreperfusion following destruction), a commonly accepted model is definedby a mono-exponential function I(t) of the intensity of the (video) echosignal against the time, with a general form:

I(t)=A·(1−e ^(-βt))

where A is a steady-state amplitude and β is a velocity term of themono-exponential function (with the time origin taken at the instantimmediately following the destruction flash). In this case the perfusionparameters are the values A and β; these values have commonly beeninterpreted as quantities proportional to a regional blood volume and ablood velocity, respectively, while the value Aβ has been interpreted asa quantity proportional to the flow.

Parametric imaging techniques are also commonly used for graphicallyrepresenting the result of the above-described quantitative analysis.For this purpose, a parametric image is built by assigning thecorresponding value of a selected perfusion parameter to each pixel.Typically, different ranges of values of the perfusion parameter arecoded with corresponding colors; the pixel values so obtained are thenoverlaid on an original image. The parametric image shows the spatialdistribution of the perfusion parameter throughout the body part underanalysis; this facilitates the identification of possible portions ofthe body part that are abnormally perfused (for example, because of apathological condition).

However, the parametric image simply provides a static representation ofthe values of the perfusion parameter. Therefore, it does not allow adirect visual perception of the perfusion process, which is normallyprovided by the playback of the original sequence of images.

A different approach is proposed in US Publication No. 2003/0114759,which is incorporated by reference. In this case, multiple single-phasesequences of images are built; each single-phase sequence is obtained byassembling all the images that were acquired at a corresponding phase ofdifferent cardiac cycles. For each single-phase sequence of images, thecorresponding pixel values (or groups thereof) are fitted by a modelfunction as above; a parametric image is again built by assigning thecorresponding values of a selected perfusion parameter (calculated fromthe model function) to each pixel. The sequence of parametric images soobtained (for the different phases of the cardiac cycle) may bedisplayed in succession. The cited document also hints to thepossibility of using the same technique for other organs that are notstrongly related to the heart cycle (such as the liver, the kidney, atransplanted organ or a limb of the body). In this case, theabove-described procedure is applied to different periods of thediagnostic process; for each period, a distinct parametric image islikewise generated from the corresponding sub-sequence of images (withthese parametric images that may again be displayed in succession). Inany case, the perfusion parameters are still formed using the values A,β and their combinations (such as A*β or A/β); alternatively, it is alsopossible to base the perfusion parameters on the error or variance ofthe corresponding model function.

The above-described solution provides some sort of indication of theperfusion changes at the different phases of the heart cycle (or moregenerally of the diagnostic process). Nevertheless, each parametricimage is still based on fixed perfusion parameters representing thecorresponding model function statically.

SUMMARY

In its general form, an embodiment of the present invention is based onthe idea of representing animated perfusion images.

More specifically, an embodiment of the present invention proposes amedical imaging system (such as based on an ultrasound scanner). Thesystem includes means for providing a sequence of recorded input images(for example, by extracting them from a storage device). Each inputimage offers a digital representation (at a corresponding instant) of abody part that was perfused with a contrast agent; particularly, eachinput image includes a plurality of visualizing values (such as a matrixof pixel/voxel values), each one representing a corresponding portion ofthe body part. Means is provided for associating each sequence ofcorresponding sets in the input images (each one including either asingle visualizing value or a group thereof) with a model function oftime (for example, through a curve-fitting process). The system furtherincludes means for generating a sequence of computed images at furtherinstants. Each computed image includes a plurality of furthervisualizing values; each one of these further visualizing values isdetermined by an instantaneous function-value, which is calculated fromthe associated model function at the corresponding further instant.Means (such as a monitor) is then provided for displaying the sequenceof computed images.

In an embodiment of the invention, a reference region is selected in theinput images; a sequence of reference values is then determined,according to the instantaneous function-values of the correspondingreference models at each (further) instant. In this case, each (further)visualizing value for a different analysis region is set by combiningthe instantaneous function-value and the corresponding reference value.

For example, each reference value is set according to the average of theinstantaneous function-values of the reference region (at the relevantinstant).

Advantageously, each visualizing value of the computed image is obtainedby subtracting the reference value from the corresponding instantaneousfunction-value.

In a proposed implementation, the instantaneous function-values (for theanalysis region and/or the reference region) correspond to the values ofthe associated model functions at the relevant instants.

In another implementation, the same instantaneous function-valuescorrespond to the integrals of the associated model functions (at therelevant instants).

In an alternative embodiment of the invention, the visualizing values ofthe computed image are set directly to the instantaneousfunction-values. As above, the instantaneous function-values maycorrespond either to the values or to the integrals of the associatedreference models at the relevant instants.

The operation of associating the model functions may be performed at thelevel of single pixel values, voxel values, or groups thereof.

A way to further improve the proposed solution is of linearizing theinput images, so as to make their visualizing values substantiallyproportional to a concentration of the contrast agent in thecorresponding portions of the body part.

In a preferred implementation of the invention, the system displays asequence of overlaid images (which are determined by overlaying thesequence of computed images on the sequence of input images).

As a further enhancement, each visualizing value in the computed imagesthat does not reach a threshold value is reset (for example, to zero).

In addition or in alternative, the visualizing values for which aquality-of-fit index does not reach a further threshold value arelikewise reset.

Advantageously, the computed images are generated by removing an offsetfrom the model functions.

As a further enhancement, the visualizing values in the computed imagesare displayed with a color-coded representation.

In a specific embodiment of the invention, one or more input images maybe discarded (for example, when they are not suitable for furtheranalysis).

An embodiment of the proposed solution is particularly advantageous whenthe sequence of input images has a frame rate that is lower than the oneof the sequence of computed images.

Preferably, in this case one or more further images are inserted intothe sequence of input images (for example, by duplicating the ones atthe closest instants), so that each computed image can be overlaid on acorresponding input image.

In a particular embodiment of the invention, the system also includesmeans (such as an imaging probe) for acquiring the input images insuccession from the body part.

Typically, the imaging probe is of the ultrasound (linear- orphased-array) type.

Another embodiment of the present invention proposes a correspondingmedical imaging method.

A further embodiment of the invention proposes a computer program forperforming the method.

A still further embodiment of the invention proposes a product embodyingthe program.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention, as well as further featuresand the advantages thereof, will be best understood by reference to thefollowing detailed description, given purely by way of a non-restrictiveindication, to be read in conjunction with the accompanying drawings.

FIG. 1 is a pictorial representation of an ultrasound scanner in whichthe solution according to an embodiment of the invention is applicable.

FIG. 2 depicts the main software and hardware components that can beused for practicing a solution according to an embodiment of theinvention.

FIG. 3 shows an exemplary application of the solution according to thisembodiment of the invention.

FIG. 4 shows a different exemplary application of the solution accordingto the same embodiment of the invention.

FIG. 5 depicts the main software and hardware components that can beused for practicing a solution according to another embodiment of theinvention.

FIG. 6 shows an exemplary application of the solution according to thisembodiment of the invention.

FIG. 7 depicts the main software and hardware components that can beused for practicing a solution according to a further embodiment of theinvention.

FIG. 8 shows an exemplary application of the solution according to thisembodiment of the invention.

DETAILED DESCRIPTION

With reference in particular to FIG. 1, a medical imaging system 100 isillustrated. Particularly, the system 100 includes an ultrasound scannerhaving a central unit 105 with a hand-held transmit-receive imagingprobe 110 (for example, of the matrix-array type). The imaging probe 110transmits ultrasound waves including a sequence of pulses (for example,having a center frequency between 2 and 10 MHz), and receivesradio-frequency (RF) echo signals resulting from the backscattering ofthe ultrasound waves; for this purpose, the imaging probe 110 isprovided with a transmit/receive multiplexer, which allows using theimaging probe 110 in the above-mentioned pulse-echo mode.

The central unit 105 houses a motherboard 115, on which the electroniccircuits controlling operation of the ultrasound scanner 100 (such as amicroprocessor, a working memory and a hard-disk drive) are mounted.Moreover, one or more daughter boards (denoted as a whole with 120) areplugged on the motherboard 115; the daughter boards 120 provide theelectronic circuits for driving the imaging probe 110 and for processingthe received echo signals. The ultrasound scanner 100 can also beequipped with a drive 125 for reading removable disks 130 (such asfloppy-disks). A monitor 135 displays images relating to the analysis inprogress. Operation of the ultrasound scanner 100 is controlled by meansof a keyboard 140, which is connected to the central unit 105 in aconventional manner; preferably, the keyboard is provided with atrackball 145 that is used to manipulate the position of a pointer (notshown in the figure) on a screen of the monitor 135.

The ultrasound scanner 100 is used to assess blood perfusion in a bodypart 150 of a patient 155. For this purpose, a contrast agent isadministered to the patient 155; the contrast agent may be providedeither with a continuous administration (by means of a pump) or as abolus (typically by hand with a syringe). Suitable contrast agentsinclude suspensions of gas bubbles in a liquid carrier; typically, thegas bubbles have diameters on the order of 0.1-5 μm, so as to allow themto pass through the capillary bed of the patient 155. The gas bubblesare generally stabilized by entraining or encapsulating the gas or aprecursor thereof into a variety of systems, including emulsifiers,oils, thickeners, sugars, proteins or polymers; stabilized gas bubblesare referred to as gas-filled microvesicles. The microvesicles includegas bubbles dispersed in an aqueous medium and bound at the gas/liquidinterface by a very thin envelope involving a surfactant, i.e., anamphiphilic material such as phospholipids (also known in this case asmicrobubbles). Alternatively, the microvesicles include suspensions inwhich the gas bubbles are surrounded by a solid material envelope formedof natural or synthetic polymers (also known in this case asmicroballoons or microcapsules). Another kind of contrast agent includessuspensions of porous microparticles of polymers or other solids, whichcarry gas bubbles entrapped within the pores of the microparticles.Examples of suitable aqueous suspensions of microvesicles, in particularmicrobubbles and microballoons, and of the preparation thereof aredescribed in EP-A-0458745, WO-A-91/15244, EP-A-0554213, WO-A-94/09829and WO-A-95/16467 (the entire disclosures of which are hereinincorporated by reference). A commercial ultrasound contrast agentcomprising gas-filled microvesicles is Sono Vue® by Bracco InternationalBV.

The imaging probe 110 is placed in contact with the skin of the patient155 in the area of the body part 150 to be analyzed. Typically, after apredetermined period (for example, a few seconds) ensuring that thecontrast agent has filled the body part 150, one or more ultrasoundpulses with high acoustic energy (flash) are applied; the acousticenergy is sufficient (such as with a mechanical index of 1-2) to causethe destruction of a significant portion of the contrast agent (forexample, at least 50%); this allows the detection of a substantialvariation of the received echo signal between the value measured rightafter the application of the destruction flash and when the body part isreplenished by the contrast agent. A series of ultrasound pulses withlow acoustic energy (such as with a mechanical index of 0.01-0.1) isthen applied, so as to involve no further destruction of the contrastagent; observation of the replenishment (or reperfusion) of the contrastagent in the body part 150 provides information about the local bloodperfusion. For this purpose, digital images representing the body part150 are acquired continuously (for example, at a rate of 10-30 imagesper second), in order to track the evolution of the perfusion processover time.

Moving now to FIG. 2, the main software and hardware components that canbe used for practicing the solution according to a simplified embodimentof the invention are denoted as a whole with the reference 200. Theinformation (programs and data) is typically stored on the hard disk andloaded (at least partially) into the working memory when the programsare running, together with an operating system and other applicationprograms (not shown in the figure). For example, the programs can beinitially installed onto the hard disk from CD-ROMs.

Particularly, a driver 203 controls the imaging probe (not shown in thefigure); for example, the imaging probe driver 203 includes a transmitbeam former and pulsers for generating the (pulsed) ultrasound waves tobe applied to the body part under analysis. The corresponding (RF) echosignals that are received from said body part are supplied to a receiveprocessor 206. Typically, the receive processor 206 pre-amplifies the RFecho signals and applies a preliminary time-gain compensation (TGC); the(analog) RF echo signals are then converted into digital values by anAnalog-to-Digital Converter (ADC), and combined into focused RF echosignals through a receive beam former. The RF echo signals so obtainedare preferably processed through further digital algorithms (such asalgorithms specific for the enhancement of contrast-agent-based echosignals) and other linear or non-linear signal conditioners (such as apost-beam-forming TGC). The RF echo signals are then demodulated,log-compressed, and then scan-converted into a video format. Thisprocess results in the registration of a sequence of input images SIi,which are stored into a corresponding repository 209 for offlineanalysis. Each input image includes a digital representation of the bodypart during the perfusion process. The input image is defined by amatrix (for example, with 512 rows and 512 columns) of visualizingvalues; each visualizing value includes a value (for example, between 0and 255) that is determined by the acoustic power of the echo signalrelating to a basic picture element (pixel) or volume element (voxel) ofthe body part.

The sequence of input images SIi is extracted from the repository 209and supplied to an image-selector 212. The image-selector 212 removesthe input images (if any) that are not suitable for further analysis;for example, the image-selector 212 skips any input image that ismisaligned (due to the motion of the patient, to his/her respiratorycycle or to the involuntary movement of the imaging probe) and whosemotion cannot be compensated (for example, because of an “out-of-plane”movement).

The sequence of input images so reduced (denoted with RSIi) is providedto an operator 214, which linearizes each input image pixel-by-pixel.More specifically, the linearization operator 214 processes each pixelvalue so as to make it directly proportional to the corresponding localconcentration of the contrast agent; this reflects the nature of thescattering of acoustic energy by a population of randomly spacedscatterers, which provides an echo-power signal proportional to thecontrast agent concentration. For example, the result can be achieved byapplying an inverse log-compression (to reverse the effect of itsapplication by the receive processor 206), and then squaring the valuesso obtained (as described in WO-A-2004/110279, the entire disclosures ofwhich is herein incorporated by reference). This operation generates asequence of linearized input images denoted with SLIi.

The sequence of linearized input images SLIi is provided to aspatial-subsampler 215. The spatial-subsampler 215 partitions eachlinearized input image into cells corresponding to a spatial resolutionof the imaging probe; each cell includes a group of adjacent pixels (orvoxels), for example, ranging from 2 to 16 along each dimension of thelinearized input images. The spatial resolution is determinedautomatically by estimating the smallest significant elements that canbe discriminated in the linearized input images (consisting of thespeckle grains that are typically visible in the input images in thecase of the ultrasound scanner); for example, this result can beachieved through a spectral analysis of the sequence of linearized inputimages SLIi along each dimension. Each linearized input image is thenreplaced by a corresponding subsampled image, which represents each cellof the linearized input image with a single value defined by an averageof its pixel values (for example, by subsampling the linearized inputimage after applying a low-pass filtering). This results in a sequenceof subsampled images SIs, which is transmitted to a curve-fittingprocessor 218.

The curve-fitting processor 218 associates each cell of the subsampledimages with an instance of a model function of time (t), which isselected according to the specific perfusion process to be represented.The instance of the model function is defined by the values of a set ofparameters thereof. The parameter values are chosen as those that bestfit the corresponding sequence of cell values (using well knownerror-minimization algorithms). The curve-fitting processor 218 thusgenerates a parameter matrix Ap, which contains the optimal values ofthese parameters for each cell.

The parameter matrix Ap is then input to an image processor 225, whichis controlled by a selection signal Sel. The image processor 225 isspecifically designed for calculating different types of instantaneousfunction-values of each model function according to the selection signalSel (as described in detail in the following); examples of theseinstantaneous function-values are the actual value of the model functionor its integral. The image processor 225 also receives a value defininga desired sampling interval Ts (for example, in the range 5-80 ms). Theimage processor 225 builds a sequence of processed images SIp. Eachprocessed image is associated to a corresponding instant defined by thesampling interval Ts, with the j-th processed image (j>=0) that isassociated to the instant t_(j)=j·Ts starting from the time origin ofthe perfusion process. For each cell, the processed image includes thevalue of the attribute of the corresponding model function at therelevant instant t_(j).

The sequence of processed images SIp is provided to a quantizer 227,which is controlled by an enabling signal EN. The quantizer 227 convertsthe continuous values of the cells into corresponding discrete values(for example, consisting of 64 or 128 levels that are uniformlydistributed between the lowest value and the highest value of all thecells). The quantizer 227 also accesses a color look-up table 230. Thecolor look-up table 230 associates all the possible levels with therepresentation of corresponding colors (that are preferably brighter asthe levels increase); for example, each color is defined by an index foraccessing a location within a palette containing its actualspecification. The quantizer 227 is enabled by asserting the signal EN;in this condition, it replaces each cell value in the processed imageswith the corresponding color representation. Conversely, when thequantizer 227 is disabled the sequence of processed images SIp passesthrough it without any change.

In any case, the sequence of processed images SIp (either quantized oras originally built) is provided to a spatial-interpolator 233. Thespatial-interpolator 233 restores the full-size of the processed images(corresponding to the one of the input images) according to aninterpolation method (such as based on the nearest neighbor, bilinear,or bicubic technique). More in detail, each processed image is convertedinto a corresponding computed image; for this purpose, the value of eachcell in the processed image is replicated for the corresponding group ofpixels (nearest neighbor interpolation method) and optionally filteredspatially (such as using a low-pass 2D or 3D spatial filter). Thesequence of computed images SIc so obtained thus represents an animationof the perfusion process; these computed images are stored into arepository 236.

The repository 236 is accessed by a player 239, which also receives thesampling interval Ts (defining the time interval between each pair ofadjacent computed images). In addition, the player 239 is supplied withan index Xs, which is selected according to the desired reproductionspeed of the sequence of computed images SIc; for example, the speedindex Xs is set to 1 for a reproduction in real time, to a value lowerthan 1 for a reproduction in slow-motion or to a value higher than 1 fora reproduction in accelerated-motion. The player 239 extracts thecomputed images in succession from the repository 239 at intervals givenby Ts/Xs. Each computed image is then passed to a monitor driver 242 forits playback (according to this frame rate).

An exemplary application of the above-described solution is shown inFIG. 3. Particularly, this application relates to a bolus administrationwithout any deliberate destruction of the contrast agent. A sequence ofinput images was acquired by the ultrasound scanner during the perfusionprocess of a mammary tumor; this sequence (shown as resulting afterdemodulation and log-compression) is denoted with 305. The input imageswere recorded at intervals of 0.52 s; for the sake of clarity, thesequence shown in the figure only includes a subset of the input images(more specifically, one input image every 2.6 s). As can be seen, thesequence of input images 305 depicts the evolution over time of the echosignal during the perfusion process. More specifically, in a wash-inphase following the administration of the contrast agent the echo signalincreases (as the contrast agent washes into the body part) until amaximum level at around the time 4 s; the echo signal then startsdecreasing in a wash-out phase of the contrast agent (extending overseveral tens of seconds). Each sequence of cell values in thecorresponding linearized images (not shown in the figure) represents theecho-power signals, which were recorded for a corresponding portion ofthe body part over time. Two exemplary sequences of these cell valuesobtained by averaging the corresponding (linearized) pixel values aredenoted in the figure with 310 a (Data set₁) for the cell identified bythe dashed arrow 312 a and with 310 b (Data set₂) for the cellidentified by the dashed arrow 312 b.

For each cell, the sequence of corresponding values is fitted by aninstance of a suitable model function (defined by the values of therespective parameters). In the example at issue, the model functionconsists of a lognormal distribution function (i.e., a normaldistribution function of the natural logarithm of the independentvariable t):

$\begin{matrix}{{B(t)} = {O + {A \cdot \frac{^{- \frac{{\lbrack{{\ln {({t - t_{0}})}} - m_{B}}\rbrack}^{2}}{2s_{B}^{2}}}}{{\left( {t - t_{o}} \right) \cdot s_{B}}\sqrt{2\pi}}}}} & {{{{{for}\mspace{14mu} t} - t_{0}} > 0},{and}} \\{{B(t)} = O} & {{{{{for}\mspace{14mu} t} - t_{0}} \leq 0},}\end{matrix}$

where t₀ represents a delay depending on the choice of a time origin forthe analysis of the perfusion process, O is an offset parameter and A isan amplitude parameter (which can be related to the relative regionaltissue blood volume); in addition, the parameters m_(B) and s_(B) arethe mean and standard deviation of the distribution of the naturallogarithms of t, respectively. The graphical representations of themodel functions associated with the sequences of cell values 310 a and310 b are denoted in the figure with 315 a (Fit₁) and 315 b (Fit₂),respectively.

In this case, the instantaneous function-values (which are used togenerate the computed images) correspond to the values of the modelfunctions over time. More specifically, for each cell the respectivevalues are calculated by evaluating the corresponding model function atthe subsequent instants t_(j). Each resulting sequence of cell valuesthen represents the evolution over time of the echo-power signals (for acorresponding portion of the body part) as defined by the respectivemodel function, instead of the actual echo-power signals that wererecorded during the perfusion process (provided by the sequence ofvalues for the same cell in the linearized input images). An exemplarysequence of computed images built as described above (at the sameinstants as the input images) is denoted with 320.

As clearly shown in the figure, the sequence of computed images 320allows a strongly enhanced perception of the perfusion process, andespecially of its pattern and kinetics (as compared to the sequence ofinput images 305). Several factors contribute to this enhancedperception. A first factor is due to the temporal smoothing in each cellof the local intensity associated with the presence of the contrastagent. A second factor is due to the spatial smoothing of the sameintensity from cell to cell (which removes any rapid fluctuation of thespeckle grains in time). Yet a third factor is due to the removal of anyresidual or cyclic movement among the input images. Indeed, for eachcell, the corresponding values are computed at fixed locations bydefinition; therefore, any motion may degrade the quality of the fittingby the model functions, but it does not appear as motion in the sequenceof computed images 320.

Preferably, the evaluation of the model function for each cell of thecomputed images is performed by removing the offset parameter O. Thisefficiently suppresses the contribution of any background echo-powersignal in the computed images. As a result, a significantly clearerperception of the dynamics of the perfusion process is achieved.

The reading of the sequence of computed images 320 is furtherfacilitated when they are displayed in color (as defined by the colorrepresentations assigned to the cells during the process of building thecomputed images). In this case, each different color bears aquantitative meaning of its own; for example, this value can be read outfrom a color-bar (not shown in the figure), which is displayed on themonitor close to the sequence of computed images 320.

It is emphasized that the selection of the sampling interval Ts(defining the temporal resolution of the sequence of computed images) isentirely independent of the acquisition rate of the input images.Therefore, the computed images may be calculated at any instants (evenwhen no input image is available in the original sequence); in this way,it is possible to optimize the sampling interval Ts, in order to smooththe rendering of the computed images (allowing the best possibleperception of the perfusion process, and especially of its kinetics andspatial distribution).

With reference now to FIG. 4, an exemplary application of theabove-described solution to the same sequence of input images 305 isshown for the selection of a different type of instantaneousfunction-value (according to the corresponding signal Sel). In thiscase, the instantaneous function-values correspond to the integrals ofthe model functions over time. More specifically, for each cell therespective values are calculated by integrating the model function B(t)minus the offset parameter O from the time origin to the subsequentinstants t_(j). These values, denoted with BI(t_(j)), represent aquantity proportional to the amount of contrast agent that had flowedthrough the portion of the body part represented by the cell:

BI(t _(j))=∫₀ ^(j) [B(t)−O]dt.

The graphical representations of the instantaneous values of thatintegral for the cell identified by the dashed arrow 312 a and for thecell identified by the dashed arrow 312 b are denoted in the figure with415 a (BI₁) and 415 b (BI₂), respectively. An exemplary sequence ofcomputed images built as described above (at the same instants as theinput images) is denoted with 420.

In this case, the sequence of computed images 420 provides an animatedrepresentation of the evolution over time of any attribute of interest.Moreover, as pointed out in the preceding application, the sequence ofcomputed images 420 provides an enhanced visual perception of theperfusion process (due to the temporal smoothing, the spatial smoothing,and the motion removal). Likewise, the subtraction of the offsetparameter O suppresses the contribution of any background echo-powersignal. In addition, the reading of the sequence of computed images 420can be further facilitated when they are displayed in color.

Moving now to FIG. 5, the main software and hardware components that canbe used for practicing a solution according to another (moresophisticated) embodiment of the invention are denoted as a whole withthe reference 500 (the elements corresponding to the ones shown in theFIG. 2 are denoted with the same references, and their explanation willbe omitted for the sake of brevity).

In this case, the sequence of processed images SIp built by the imageprocessor 225 (according to the selection signal Sel) is provided to avalue mask generator 505; the value mask generator 505 also receives apredefined threshold value Th_(v) for the cell values (for example, from0 to 5% of a maximum allowable value). The value mask generator 505creates a sequence of value masks SMv. Each value mask is obtained fromthe corresponding processed image by assigning (to each cell) the logicvalue 1 if its value is strictly higher than the threshold value Th_(v)or the logic value 0 otherwise.

At the same time, the sequence of subsampled images SIs (from thespatial subsampler 215) and the parameter matrix Ap (from thecurve-fitting processor 218) are supplied to a quality evaluator 510.For each cell, the quality evaluator 510 determines a Quality of Fit(QOF) index indicative of the quality of the curve-fitting processapplied by the module 218; for example, the QOF index may be defined asa percentage:

${{Q\; O\; F} = {100 \cdot \left( {1 - \frac{SSE}{SST}} \right)}},$

where the terms SSE and SST are calculated as follows. Particularly, theterm SSE is the sum of the squares of the differences between the valuesin the subsampled images and the corresponding (predicted) values of themodel function; in other words, indicating with S_(i) the cell value inthe subsampled image, corresponding to the input image acquired at theinstant t_(i) (with i=1 . . . N), and with P_(i) the value of the modelfunction at the same instant t_(i), we have:

${{SSE} = {\sum\limits_{i = 1}^{N}\left( {S_{i} - P_{i}} \right)^{2}}},$

The term SST is the sum of the squares of the differences of the samevalues of the subsampled images from their mean value (AVG):

${{SST} = {\sum\limits_{i = 1}^{N}\left( {S_{i} - {AVG}} \right)^{2}}},{{{with}\mspace{14mu} {AVG}} = {\frac{1}{N} \cdot {\sum\limits_{i = 1}^{N}{S_{i}.}}}}$

It is then evident that the higher the QOF index, the more accurate thecurve-fitting process (up to the ideal value of 100% for a perfectmatching of the model function to the available information).

The quality evaluator 510 thus generates a quality matrix Aq, whichincludes the corresponding QOF index for each cell. The quality matrixAq is passed to a quality mask generator 515, which also receives apredefined threshold value Th_(q) for the QOF indexes (for example,between 40% and 60%). The quality mask generator 515 converts thequality matrix Aq into a corresponding quality mask Mq; for thispurpose, the quality mask generator 515 assigns (to each cell) the logicvalue 1 if its QOF index is strictly higher than the threshold valueTh_(q) or the logic value 0 otherwise.

A multiplier operator 520 receives the sequence of value masks SMv (fromthe value mask generator 505) and the quality mask Mq (from the qualitymask generator 515). The operator 520 multiplies each value mask by thequality mask Mq cell-by-cell, so as to generate a sequence ofcorresponding (total) masks SM. In this way, each cell of the masks soobtained will have the logic value 0 if the cell value of the respectiveprocessed image is lower than the threshold value Th_(v) or if thecorresponding QOF index is lower than the threshold value Th_(q), and itwill have the logic value 1 otherwise.

The sequence of masks SM is input to a further multiplier operator 525,which also receives the sequence of processed images SIp from the imageprocessor 225. The operator 525 multiplies each processed image by thecorresponding mask cell-by-cell, so as to obtain a sequence of maskedprocessed images SMIp; as a result, each masked processed image onlyincludes the cell values of the corresponding processed image that arehigher than the threshold value Th_(v) and whose QOF index is higherthan the threshold value Th_(q) at the same time (while the other cellvalues are reset to 0). The sequence of masked processed images SMIp isthen provided to the quantizer 227, so as to obtain a correspondingsequence of masked computed images SMIc from the spatial interpolator233 as described above.

The sequence of masks SM is also supplied to an inverter 530, whichgenerates a corresponding sequence of inverted masks SM (by exchangingthe logic values 0 and 1). The sequence of inverted masks SM is thenprovided to a further spatial-interpolator 535 (which acts identicallyto the spatial-interpolator 233) for restoring the full-size of theinverted masks (corresponding to the one of the input images). Thisprocess results in a sequence of interpolated inverted masks SMi. At thesame time, the reduced sequence of input images RSIi (generated by theimage-selector 212) is provided to an image-duplicator 540 (in additionto the linearization operator 214); the image-duplicator 540 alsoreceives the sampling interval Ts (defining the time interval betweeneach pair of adjacent masked computed images). When the time intervalbetween each pair of available input images is higher than the one ofthe masked computed images, the image-duplicator 540 adds one or morenew images to the reduced sequence of input images RSIi by duplicatingthe closest input images as needed to match the number of input imagesto the number of computed images. This operation is aimed at having (foreach masked computed image at the instant t_(j)) a corresponding inputimage acquired or duplicated at the same instant t_(j). The sequence ofsynchronized input images so obtained (denoted with SIt) is input to amultiplier operator 545, which also receives the sequence ofinterpolated inverted masks SMi from the spatial-interpolator 535. Theoperator 545 multiplies each synchronized input image by thecorresponding interpolated inverted mask pixel-by-pixel, so as to obtaina sequence of masked input images SMIi.

An operator 550 adds each masked computed image (from thespatial-interpolator 233) and the corresponding masked input image (fromthe multiplier operator 530) pixel-by-pixel, so as to obtain a sequenceof overlaid images SIo. In this way, each pixel value of the inputimages is overridden by the corresponding value in the associatedcomputed image if and only if the latter has a significant value (i.e.,higher than the threshold value Th_(v)) and corresponds to an acceptablylevel of fit quality (i.e., its QOF index is higher than the thresholdvalue Th_(q)). The sequence of overlaid images SIo is stored into therepository 236, and then provided to the player 239 that controls itsplayback as described before.

In this way, by tuning the threshold values Th_(v), Th_(q) it ispossible to optimize the quality of the visualization with the minimumimpact on the original images. Moreover, it should be noted that theapplication of the value masks and/or of the quality mask may be avoidedby simply setting the threshold value Th_(v) or the threshold valueTh_(q), respectively, to zero (so as to obtain corresponding all-onemasks that do not affect the computed images).

With reference now to FIG. 6, an exemplary application of theabove-described solution to the same sequence of input images 305 isshown. Particularly, the sequence of computed images 320 described-abovecan be associated with a corresponding sequence of masks 605 (by settingthe threshold value Th_(v) to 1% of the maximum value in the sequence ofprocessed images and the threshold value Th_(q) to 0). The overlay ofthe computed images (multiplied by the corresponding masks) on the inputimages (multiplied by the corresponding interpolated inverted masks, notshown in the figure) generates a sequence of overlaid images 610. Asclearly shown in the figure, the input images are still visible as abackground; therefore, an enhanced visual perception of the perfusionprocess is provided. In this respect, it should be noted that thethreshold value Th_(v) defines the degree of overlay of the computedimages; therefore, the suggested range of values (0-5%) preserves anysignificant information in the computed images and at the same timeavoids overriding the original images when it is not strictly necessary.As a result, the sequence of overlaid images 610 provides an enhancedvisual perception of the perfusion process (as noted in the precedingembodiment of the invention), which is now contextualized on the actualrepresentation of the body part under analysis.

Proceeding to FIG. 7, the main software and hardware components that canbe used for practicing a solution according to a further embodiment ofthe invention are denoted as a whole with the reference 700. For thesake of simplicity, the additional features of this embodiment of theinvention will be described with reference to the structure proposed inthe FIG. 2 (wherein the corresponding elements are denoted with the samereferences and their explanation is omitted); however, it is expresslyintended that the same features may be added to the more sophisticatedimplementation described above with reference to the FIG. 5.

Particularly, a drawing module 705 is used to define a reference region,a delimitation region and an analysis region on one of the input images,selected by the image selector 212 out of the repository 209. Thereference region represents an area with well-defined characteristics(for example, outlining a tissue region deemed to be healthy); on theother hand, the delimitation region defines a region of interest (ROI)of the perfusion process (for example, outlining a tissue region deemedto be suspicious or known to be a lesion), whereas the analysis regiondefines a region that is chosen for analysis within the delimitationregion. This operation generates a reference mask Mr (for the referenceregion), a delimitation mask Md (for the delimitation region), and ananalysis mask Ma (for the analysis region). Each mask Mr, Md and Maincludes a matrix of binary values with the same size as the inputimages. In each of the three masks Mr, Md and Ma, the binary valuesinside their corresponding region are assigned the logic value 1,whereas the binary values outside their corresponding region areassigned the logic value 0.

A multiplier operator 710 receives the sequence of input images RSIi(reduced as described-above by the image selector 212) and thedelimitation mask Md. The operator 710 multiplies each input image bythe delimitation mask Md pixel-by-pixel, so as to reset all the pixelvalues that are outside the delimitation region to 0 (while the otherpixel values remain unchanged). The reduced sequence of input images soupdated (differentiated by the prime symbol, i.e., RSIi′) is thenprovided to the linearization operator 214, so as to repeat the sameoperations described above. In this case, the sequence of processedimages SIp built by the image processor 225 (for the whole delimitationregion) is saved into a corresponding repository (not shown in thefigure); therefore, the same information may be used (as described inthe foregoing) for a different analysis region that is drawn inside thesame delimitation region (without having to recalculate it).

The reference mask Mr and the analysis mask Ma are instead provided bythe image selector 212 to a simplified spatial-subsampler 715.Particularly, the spatial-subsampler 715 partitions the reference maskMr and the analysis mask Ma into a subsampled reference mask Msr and asubsampled analysis mask Msa, respectively (in a way similar to thespatial-subsampler 215 subsamples the linearized images); in this case,however, each cell value of the subsampled masks Msr and Msa is roundedoff, so as to ensure that it always contains the values 0 or 1 only.

An average operator 720 receives the subsampled reference mask Msr andthe sequence of subsampled images SIs (from the spatial subsampler 215).The average operator 720 generates a corresponding sequence of averagevalues SVa. For this purpose, the average operator 720 multiplies eachsubsampled image by the subsampled reference mask Msr cell-by-cell. Foreach subsampled image, the values thus obtained are added in a buffer;the corresponding average value is then obtained by dividing the finalvalue of the buffer by the number of non-zero values in the subsampledreference mask Msr; in this way, each average value includes the averageof the cell values of the reference region in the correspondingsubsampled image.

A further curve-fitting processor 725 (exactly the same as thecurve-fitting processor 218) associates the sequence of average valuesSVa with an instance of the relevant model function (defined by thevalues of its parameters); the curve-fitting processor 725 thusgenerates the optimal values of these parameters (for the wholereference region), denoted with Vp. The parameter values Vp are input toan evaluator 730, which also receives the sampling interval Ts (definingthe time between each pair of adjacent processed images). The evaluator730 generates a sequence of reference values SVr, which is synchronizedwith the sequence of processed images SIp built by the image processor225; more specifically, for each instant t_(j)=j·Ts the evaluator 730sets the reference value to the corresponding instantaneous value of themodel function defined by the parameter values Vp.

In parallel, a multiplier operator 735 receives the subsampled analysismask Msa (from the spatial subsampler 715) and the sequence of processedimages SIp (from the image processor 225). The operator 735 multiplieseach processed image by the subsampled analysis mask Msa cell-by-cell,so as to generate a sequence of corresponding analysis images SIa; inthis way, each analysis image only includes the cell values of thecorresponding processed image that are inside the analysis region(defined by the subsampled analysis mask Msa), while the other cellvalues are reset to 0.

A subtraction operator 740 receives the sequence of analysis images SIa(from the multiplier operator 735) and the sequence of reference valuesSVr (from the evaluator 730), which are synchronous to each other. Theoperator 740 subtracts, for each instant in the sequence of analysisimages SIa, the reference value from each of the cell values of thecorresponding analysis image; this operation results in a sequence ofcorresponding updated processed images, which are differentiated by theprime symbol (i.e., SIp′); the sequence of processed images SIp′ isprovided to the quantizer 227 to repeat the same operations described inthe foregoing.

A proposed embodiment of the invention advantageously enhances anydifferences in perfusion kinetics of the analysis region of the bodypart under examination, compared to the perfusion kinetics of thereference region; this strongly facilitates the identification ofseveral pathologies. For example, this approach is particularly usefulfor the enhanced characterization of dynamic vascular patterns (DVP) inliver diseases. Indeed, several liver diseases may induce differentperfusion kinetics of the contrast agent, which in general differ fromthe perfusion kinetics observable in normal parenchyma. These differentkinetics can be appreciated, for instance, during the wash-in andwash-out phases of the contrast agent provided with a bolusadministration.

An exemplary application of the above-described solution is shown inFIG. 8. Particularly, this application again relates to a bolusadministration without any deliberate destruction of the contrast agent.A sequence of input images was acquired by the ultrasound scanner duringthe perfusion process of a liver with a hypervascular metastasis; thissequence, shown as resulting after demodulation and log-compression, isdenoted with 805 (for the sake of simplicity, only one input image every3.2 s is illustrated). A reference region 810, deemed to representnormal parenchyma, is drawn by an operator in one selected input image(and reproduced in every other input image in this example); theoperator also selects an analysis region 815, which identifies anddelimits a suspected hypervascular metastasis.

Curve 820 (Reference) represents the sequence of reference values thatare obtained by averaging the (linearized) pixel values of the sequenceof input images 805 in the reference region 810. This sequence ofreference values is fitted by an instance of a suitable model function(including the lognormal distribution function in the example at issue);the corresponding graphical representation is denoted in the figure with825 (Fitted reference). The figure also shows a curve 830 (Data), whichrepresents an exemplary sequence of cell values obtained by averagingthe corresponding linearized pixel values for a cell identified by adashed arrow 832 within the analysis region 815. This sequence of cellvalues is fitted by another instance of the same model function, whosegraphical representation is denoted in the figure with 835 (Fitteddata).

In this case, the computed images are generated by calculating (at eachcell) the value of the corresponding model function minus the referencevalue at the same instant. For example, again considering the cellidentified by the dashed arrow 832, the desired values are calculated bysubtracting the model function 825 from the model function 835; a curverepresenting the sequence of these cell values is denoted in the figurewith 840 (Fitted data—Fitted reference).

As can be seen, the result of the operation may be positive or negative,depending on the instantaneous values of the two model functions 825 and835. Particularly, their difference is about zero when the value of themodel function 835 (for the analysis region) is substantially the sameas the corresponding reference value (i.e., the same as the average ofthe pixel values in the reference region at the same instant);conversely, the difference is positive when the value of the modelfunction 835 is higher than the corresponding reference value, or it isnegative otherwise. The cell values obtained as indicated above are thendisplayed according to a bi-polar palette look-up table 845 (of thegray-scale type in the example at issue). Therefore, each cell value isat an intermediate gray level when the value of the model function 835is substantially the same as the corresponding reference value; on theother hand, the pixel is brighter or darker when the value of the modelfunction 835 is higher or lower, respectively, than the correspondingreference value. An exemplary sequence of computed images built asdescribed above (at the same instants as the input images) is denotedwith 850.

In the example at issue, the hypervascular metastatis appears inbrighter levels of gray at early times (between 4 s and 9 s), turninginto darker levels later on (after roughly 10 s). This behavior clearlydiffers from the one of a region in normal parenchyma, wherein thepixels would have remained at the intermediate gray level. Therefore,imaging the liver according to the present embodiment of the inventionmakes the DVPs of different liver lesions much more conspicuous thanwhen shown unprocessed.

Modifications

Naturally, in order to satisfy local and specific requirements, a personskilled in the art may apply to the solution described above manymodifications and alterations. Particularly, although one or moreembodiments of the present invention have been described with a certaindegree of particularity, it should be understood that various omissions,substitutions and changes in the form and details as well as otherembodiments are possible; moreover, it is expressly intended thatspecific elements and/or method steps described in connection with anydisclosed embodiment of the invention may be incorporated in any otherembodiment as a general matter of design choice.

Particularly, similar considerations apply if the ultrasound scanner hasa different structure or includes other units. In addition, it ispossible to use equivalent contrast agents or the body part may beperfused in another way (with either the destruction flash or not).Likewise, the images can have a different format, or the pixels can berepresented with other values; alternatively, it is also possible totake into account a portion of the matrix of pixel values only (or moregenerally any other plurality of visualizing values).

Moreover, the principles of the one or more embodiments of the inventionshould not be limited to the described model functions; for example, ina perfusion process with a destruction flash it is possible to fit thesequence of corresponding cell values by an S-shape function (asdescribed in WO-A-2004/110279, the entire disclosure of which is hereinincorporated by reference), or by a mono-exponential function (asdescribed in the cited article by Wei et al.), and the like.

Of course, any other equivalent technique for associating the suitableinstance of the model function with each sequence of cell values istenable (such as based on neural networks). Moreover, even though one ormore embodiments of the present invention have been described in theforegoing with specific reference to offline analysis of the availableinformation, their application in real-time is not excluded. Forexample, in a different implementation of an embodiment of the inventionthe input images are processed as soon as a sub-set of them allowing asignificant curve-fitting is available (for example, including 7-12input images, such as 10). Afterwards, each cell of this sub-set ofinput images is associated with the corresponding instance of the chosenmodel function; this allows calculating a first computed image (at thebeginning of the process), which is preferably overlaid on the firstinput image. This first computed image so obtained may now be displayedas above, with a slight delay with respect to the actual acquisitioninstant of the corresponding input image (i.e., assuming an acquisitionrate of the input images equal to 10 Hz, after 10/10 Hz=1 s in theexample at issue). From now on, the same operation is continuouslyrepeated for any new input image that is acquired. For this purpose, themodel functions are recalculated at every iteration of the processaccording to the new information available. This result may be achievedtaking into account all the input images so far acquired (therebyproviding the highest possible accuracy); alternatively, thecurve-fitting process is always applied only to the last 10 input imagesavailable (thereby increasing the processing speed but at the cost ofreduced accuracy). In both cases, the model function preferably takesthe form of cubic-spline filtering or median filtering, so as to makethe computational complexity of the curve-fitting process acceptableeven for real-time applications.

Similar considerations apply if the computed images (or the overlaidimages) are printed, or more generally displayed in any other form.

Alternatively, it is possible to select the reference region and/or theanalysis region with different procedures, or the reference region maybe chosen according to other criteria; moreover, it is evident that thedefinition of the delimitation region is not strictly necessary, and itmay be omitted in some implementations. Furthermore, the referencevalues may be determined with equivalent procedures. For example,although it is computationally advantageous to consolidate the pixelvalues of the reference region and then associate the resulting sequenceof average values with a suitable instance of the desired model function(as described above), the possibility of inverting the order of theseoperations is contemplated. More in detail, all the cell values of thereference region may be associated with the corresponding modelfunctions; for each instant, these model functions are evaluated and theaverage of the resulting values are then calculated.

In any case, nothing prevents consolidating the available informationinto the reference values with other algorithms (for example, byapplying correlation, deconvolution or spectral analyses).

Moreover, the instantaneous function-values relating to the analysisregion and the reference values may be combined in any other way (forexample, by adding, multiplying, or dividing them).

Even though in the preceding description reference has been made tospecific instantaneous function-values of the reference models, this isnot to be intended as a limitation. For example, it is possible to basethe instantaneous function-values for the analysis region, for thereference region or for both of them on the derivatives of theassociated model functions; moreover, the possibility is not excluded ofcombining instantaneous function-values of different types. Similarconsiderations apply to the embodiments of the invention wherein thecomputed images are generated from the instantaneous function-values ofthe model functions directly (with no combination with any referencevalues). More generally, a solution according to an embodiment of theinvention may also be used to represent the evolution over time of anyother attributes or combination of attributes determined by whateverinstantaneous function-values, which are calculated from the modelfunctions at the relevant instants.

Optionally, the spatial-subsampler may partition each input image intocells that differ in size; for example, this result is achieved by meansof a multi-scale analysis technique (such as a quadtree decomposition).However, the fitting by the desired model function may be applied oncells of arbitrary size (down to a single pixel/voxel).

Alternatively, the step of linearizing the input images may be omittedbefore spatial subsampling and curve-fitting, and a possibly different,more suitable, model function may be used in the curve-fitting processorin this case; in addition, it is possible to apply motion compensationto the sequence of input images to improve the accuracy of the fittingby the model functions.

Similar considerations apply if the computed images are overlaid on theinput images with an equivalent algorithm. In any case, as describedabove, a simplified embodiment of the invention wherein the computedimages are displayed directly (without being overlaid on the inputimages) is feasible.

In a different embodiment of the invention, the threshold value forbuilding the value masks may be set to different values, or it may bedetermined dynamically on the basis of a statistical analysis of thecell values in the computed images.

Alternatively, it is possible to define the quality of the curve-fittingprocess with any other indicator (for example, a simple average of thecorresponding differences); moreover, in this case as well the thresholdvalue for building the quality mask may be set to different values (evendetermined dynamically).

Moreover, only the value masks or only the quality mask may besupported; in any case, the possibility of summing the whole computedimages to the input images directly (without the use of any mask) is notexcluded.

An implementation wherein the offset is not removed from the modelfunction is also within the scope of one or more embodiments of theinvention.

Similar considerations apply if the cell values in the processed imagesare partitioned in any other number of ranges (also non linearlydistributed) for the association with the corresponding colors;moreover, it should be noted that the term color as used herein is alsointended to include different tonalities, and any other visual clues.However, one or more embodiments of the present invention have equalapplication to black-and-white or grayscale representations of thecomputed images.

Alternatively, the selection of the input images to be discarded may beimplemented with different algorithms (aimed at optimizing the accuracyof the fitting by the model functions). In any case, nothing preventsusing all the available input images for fitting them by the modelfunctions.

Likewise, other image-duplication techniques may be used for equalizingthe rate of the sequence of input images with the one of the sequence ofcomputed images, such as interpolation, extrapolation, or decimation.Without departing from the principles of one or more embodiments of theinvention, the sequence of input images and the sequence of computedimages may have arbitrary timing. However, the overlay of the computedimages on the input images may also be implemented independently oftheir time synchrony.

In any case, the imaging probe may be of a different type (such as ofthe linear-, convex-, or phased-array type), or the input images may beacquired with another modality (for example, using Doppler-basedalgorithms). Alternatively, the medical imaging system includes anultrasound scanner and a distinct computer (or any equivalent dataprocessing entity); in this situation, the measured data is transferredfrom the ultrasound scanner to the computer for its processing (forexample, through a removable disk, a memory key, or a networkconnection).

In any case, a solution according to an embodiment of the presentinvention lends itself to be used in any other medical imaging system,such as based on Magnetic Resonance Imaging (MRI) or X-ray ComputedTomography (CT).

Similar considerations apply if the program (which may be used toimplement one or more embodiments of the invention) is structured in adifferent way, or if additional modules or functions are provided;likewise, the memory structures may be of other types, or may bereplaced with equivalent entities (not necessarily consisting ofphysical storage media). Moreover, a proposed solution according to anembodiment of the invention lends itself to be implemented with anequivalent method (having similar or additional steps, even in adifferent order). In any case, the program may take any form suitable tobe used by or in connection with any data processing system, such asexternal or resident software, firmware, or microcode (either in objectcode or in source code). Moreover, the program may be provided on anycomputer-usable medium; the medium may be any element suitable tocontain, store, communicate, propagate, or transfer the program.Examples of such medium are fixed disks (where the program can bepre-loaded), removable disks, tapes, cards, wires, fibers, wirelessconnections, networks, broadcast waves, and the like; for example, themedium may be of the electronic, magnetic, optical, electromagnetic,infrared, or semiconductor type.

In any case, a solution according to an embodiment of the presentinvention lends itself to be carried out with a hardware structure (forexample, integrated in a chip of semiconductor material), or with acombination of software and hardware.

1. A medical imaging system including: means for providing a sequence ofrecorded input images each one offering a digital representation at acorresponding instant of a body part being perfused with a contrastagent, each input image including a plurality of visualizing values eachone representing a corresponding portion of the body part, means forassociating each sequence of corresponding sets in the input images ofat least one visualizing value with a model function of time, means forgenerating a sequence of computed images at further instants, eachcomputed image including a plurality of further visualizing values eachone being determined by an instantaneous function-value which iscalculated from the associated model function at the correspondingfurther instant, and means for displaying the sequence of computedimages.
 2. The system according to claim 1, wherein the means forgenerating the sequence of computed images includes means for selectinga reference region in the input images, means for generating a sequenceof reference values at the further instants each one based on otherinstantaneous function-values being calculated from the model functionsassociated with the visualizing values of the reference region at thecorresponding further instant, and means for setting each furthervisualizing value according to a combination of the instantaneousfunction-value and the reference value at the corresponding furtherinstant.
 3. The system according to claim 2, wherein the means forgenerating the sequence of reference values includes means for settingeach reference value according to an average of the other instantaneousfunction-values at the corresponding further instant.
 4. The systemaccording to claim 2, wherein the means for setting each furthervisualizing value includes means for subtracting the reference valuefrom the instantaneous function-value at the corresponding furtherinstant.
 5. The system according to claim 2 4, wherein eachinstantaneous function-value and/or each other instantaneousfunction-value corresponds to the value of the associated model functionat the corresponding further instant.
 6. The system according to claim2, wherein each instantaneous function-value and/or each otherinstantaneous function-value corresponds to the integral of theassociated model function at the corresponding further instant.
 7. Thesystem according to claim 1, wherein the means for determining thesequence of computed images includes means for setting each furthervisualizing value to the instantaneous function-value, the instantaneousfunction-value corresponding to the value of the associated modelfunction at the corresponding further instant.
 8. The system accordingto claim 1, wherein the means for generating the sequence of computedimages includes means for setting each further visualizing value to theinstantaneous function-value, the instantaneous function-valuecorresponding to the integral of the associated model function at thecorresponding further instant.
 9. The system according to claim 1,wherein each set of at least one visualizing value consists of a singlepixel value, a single voxel value, a plurality of pixel values, or aplurality of voxel values.
 10. The system according to claim 1, whereinthe means for associating includes means for linearizing each inputimage to make each visualizing value thereof substantially proportionalto a concentration of the contrast agent in the corresponding portion ofthe body part.
 11. The system according to claim 1, further including:means for generating a sequence of overlaid images by overlaying thesequence of computed images on the sequence of input images, and meansfor displaying the sequence of overlaid images.
 12. The system accordingto claim 11, wherein the means for generating the sequence of overlaidimages includes means for resetting each further visualizing value notreaching a threshold value.
 13. The system according to claim 11,wherein the means for generating the sequence of overlaid imagesincludes: means for estimating an indicator of a quality of eachassociation, and means for resetting each further visualizing valuehaving the corresponding indicator not reaching a further thresholdvalue.
 14. The system according to claim 1, wherein the means forgenerating the sequence of computed images further includes means forremoving an offset from the associated model function.
 15. The systemaccording to claim 1, wherein the means for generating the sequence ofcomputed images further includes means for associating a plurality ofpredefined colors with corresponding ranges of values of the furthervisualizing values and means for replacing each further visualizingvalue with a representation of the corresponding color.
 16. The systemaccording to claim 1, wherein the means for providing the sequence ofinput images includes means for discarding at least one of the inputimages.
 17. The system according to claim 11, wherein a rate of thesequence of input images is lower than a rate of the sequence ofcomputed images.
 18. The system according to claim 17, wherein the meansfor generating the sequence of overlaid images includes means forinserting at least one further input image into the sequence of inputimages to equalize the rate of the sequence of input images with therate of the sequence of computed images, each computed image beingoverlaid on a corresponding input image.
 19. The system according toclaim 1, wherein the means for providing the sequence of input imagesincludes means for acquiring the input images in succession from thebody part.
 20. The system according to claim 19, wherein the means foracquiring the input images includes means for transmitting ultrasoundwaves and for recording corresponding echo signals.
 21. A medicalimaging method including the steps of: providing a sequence of recordedinput images each one offering a digital representation at acorresponding instant of a body part being perfused with a contrastagent, each input image including a plurality of visualizing values eachone representing a corresponding portion of the body part, associatingeach sequence of corresponding sets in the input images of at least onevisualizing value with a model function of time, generating a sequenceof computed images at further instants, each computed image including aplurality of further visualizing values each one being determined by aninstantaneous function-value which is calculated from the associatedmodel function at the corresponding further instant, and displaying thesequence of computed images.
 22. A computer program for performing themethod of claim 21 when the computer program is executed on a dataprocessing system.
 23. A computer program product including acomputer-usable medium embodying a computer program, the computerprogram when executed on a data processing system causing the system toperform a medical imaging method, wherein the method includes the stepsof: providing a sequence of recorded input images each one offering adigital representation at a corresponding instant of a body part beingperfused with a contrast agent, each input image including a pluralityof visualizing values each one representing a corresponding portion ofthe body part, associating each sequence of corresponding sets in theinput images of at least one visualizing value with a model function oftime, generating a sequence of computed images at further instants, eachcomputed image including a plurality of further visualizing values eachone being determined by an instantaneous function-value which iscalculated from the associated model function at the correspondingfurther instant, and displaying the sequence of computed images.